97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
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import glob
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import math
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import numpy as np
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import os
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from scipy.io import wavfile
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from torch.utils.data import IterableDataset
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import torch.nn.functional as F
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from enhancer.utils.random import create_unique_rng
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from enhancer.utils.io import Audio
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class VctkDataset:
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def __init__(self):
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pass
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def train_loader(self):
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pass
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def valid_loader(self):
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pass
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def test_loader(self):
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pass
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class Vctk(IterableDataset):
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"""Dataset object for Voice Bank Corpus (VCTK) Dataset"""
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def __init__(self,clean_path,noisy_path,duration=1.0,sampling_rate=48000):
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if not os.path.isdir(clean_path):
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raise ValueError(f"{clean_path} is not a valid directory")
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if not os.path.isdir(noisy_path):
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raise ValueError(f"{clean_path} is not a valid directory")
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self.sampling_rate = sampling_rate
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self.clean_path = clean_path
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self.noisy_path = noisy_path
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self.files_duration = self.get_matching_files_duration()
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self.wav_samples = list(self.files_duration.keys())
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self.duration = max(1.0,duration)
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self.audio = Audio(self.sampling_rate,mono=True,return_tensor=True)
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def get_matching_files_duration(self):
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matching_wavfiles_dur = dict()
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clean_filenames = [file.split('/')[-1] for file in glob.glob(os.path.join(self.clean_path,"*.wav"))]
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noisy_filenames = [file.split('/')[-1] for file in glob.glob(os.path.join(self.noisy_path,"*.wav"))]
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common_filenames = np.intersect1d(noisy_filenames,clean_filenames)
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for file_name in common_filenames:
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sr_clean, clean_file = wavfile.read(os.path.join(self.clean_path,file_name))
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sr_noisy, noisy_file = wavfile.read(os.path.join(self.noisy_path,file_name))
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if ((clean_file.shape[-1]==noisy_file.shape[-1]) and
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(sr_clean==self.sampling_rate) and
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(sr_noisy==self.sampling_rate)):
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matching_wavfiles_dur.update({file_name:(clean_file.shape[-1]/self.sampling_rate)})
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return matching_wavfiles_dur
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def __iter__(self):
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rng = create_unique_rng(12) ##pass epoch number here
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while True:
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file_name,*_ = rng.choices(self.wav_samples,k=1,
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weights=[self.files_duration[file] for file in self.wav_samples])
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file_duration = self.files_duration.get(file_name)
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start_time = round(rng.uniform(0,file_duration- self.duration),2)
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data = self.prepare_segment(file_name,start_time)
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yield data
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def prepare_segment(self,file_name:str, start_time:float):
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clean_segment = self.audio(os.path.join(self.clean_path,file_name),
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offset=start_time,duration=self.duration)
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noisy_segment = self.audio(os.path.join(self.noisy_path,file_name),
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offset=start_time,duration=self.duration)
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clean_segment = F.pad(clean_segment,(0,int(self.duration*self.sampling_rate-clean_segment.shape[-1])))
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noisy_segment = F.pad(noisy_segment,(0,int(self.duration*self.sampling_rate-noisy_segment.shape[-1])))
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return {"clean": clean_segment,"noisy":noisy_segment}
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def __len__(self):
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return math.ceil(sum(self.files_duration.values())/self.duration)
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